Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4501-2023
https://doi.org/10.5194/gmd-16-4501-2023
Development and technical paper
 | 
10 Aug 2023
Development and technical paper |  | 10 Aug 2023

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik

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Cited articles

Agarwal, N., Kondrashov, D., Dueben, P., Ryzhov, E., and Berloff, P.: A Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models, J. Adv. Model. Earth Sy., 13, e2021MS002537, https://doi.org/10.1029/2021MS002537, 2021. a
Arcomano, T., Szunyogh, I., Wikner, A., Pathak, J., Hunt, B. R., and Ott, E.: A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics-Based Numerical Model, J. Adv. Model. Earth Sy., 14, e2021MS002712, https://doi.org/10.1029/2021MS002712, 2022. a, b
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein generative adversarial networks, in: International conference on machine learning, PMLR, 214–223, https://doi.org/10.48550/arXiv.1701.07875, 2017. a
Arnold, H. M., Moroz, I. M., and Palmer, T. N.: Stochastic parametrizations and model uncertainty in the Lorenz’96 system, Philosophical Transactions of the Royal Society A: Mathematical, Phys. Eng. Sci., 371, 20110479, https://doi.org/10.1098/rsta.2011.0479, 2013. a, b, c, d
Bahdanau, D., Cho, K., and Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv [preprint], https://doi.org/10.48550/arXiv.1409.0473, 2014. a
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Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.